Orphan Receptor TR3 Enhances p53 Transactivation and Represses DNA Double-Strand Break Repair in Hepatoma Cells under Ionizing Radiation
Bibliographic record
Abstract
In response to ionizing radiation (IR)-induced DNA double-strand breaks (DSB), cells elicit an evolutionarily conserved checkpoint response that induces cell cycle arrest and either DNA repair or apoptosis, thereby maintaining genomic stability. DNA-dependent protein kinase (DNA-PK) is a central enzyme involved in DSB repair for mammalian cells that comprises a DNA-PK catalytic subunit and the Ku protein, which act as regulatory elements. DNA-PK also functions as a signaling molecule to selectively regulate p53-dependent apoptosis in response to IR. Herein, we demonstrate that the orphan nuclear receptor TR3 suppresses DSB repair by blocking Ku80 DNA-end binding activity and promoting DNA-PK-induced p53 activity in hepatoma cells. We find that TR3 interacts with Ku80 and inhibits its binding to DNA ends, which then suppresses DSB repair. Furthermore, TR3 is a phosphorylation substrate for DNA-PK and interacts with DNA-PK catalytic subunit in a Ku80-independent manner. Phosphorylated TR3, in turn, enhances DNA-PK-induced phosphorylation and p53 transcription activity, thereby enhancing IR-induced apoptosis in hepatoma cells. Together, our findings reveal novel functions for TR3, not only in DSB repair regulation but also in IR-induced hepatoma cell apoptosis, and they suggest that TR3 is a potential target for cancer radiotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".